Overview

Dataset statistics

Number of variables14
Number of observations2797
Missing cells31
Missing cells (%)0.1%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory594.8 KiB
Average record size in memory217.8 B

Variable types

Numeric9
Categorical4
Text1

Alerts

Dataset has 2 (0.1%) duplicate rowsDuplicates
Waterfront is highly imbalanced (94.9%)Imbalance
View is highly imbalanced (73.5%)Imbalance
Condition has 31 (1.1%) missing valuesMissing
sqftBase has 1650 (59.0%) zerosZeros
YrRenov has 1616 (57.8%) zerosZeros

Reproduction

Analysis started2024-05-16 16:35:04.142090
Analysis finished2024-05-16 16:35:22.174411
Duration18.03 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Bedrooms
Real number (ℝ)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3875581
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:22.471271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90458359
Coefficient of variation (CV)0.26703117
Kurtosis1.0264785
Mean3.3875581
Median Absolute Deviation (MAD)1
Skewness0.47633669
Sum9475
Variance0.81827147
MonotonicityNot monotonic
2024-05-16T12:35:22.650891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1266
45.3%
4 905
32.4%
2 345
 
12.3%
5 205
 
7.3%
6 41
 
1.5%
1 25
 
0.9%
7 9
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 25
 
0.9%
2 345
 
12.3%
3 1266
45.3%
4 905
32.4%
5 205
 
7.3%
6 41
 
1.5%
7 9
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 9
 
0.3%
6 41
 
1.5%
5 205
 
7.3%
4 905
32.4%
3 1266
45.3%
2 345
 
12.3%
1 25
 
0.9%

Bathrooms
Real number (ℝ)

Distinct21
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1197712
Minimum0.75
Maximum5.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:22.904036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum5.75
Range5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.74812036
Coefficient of variation (CV)0.35292505
Kurtosis0.64591642
Mean2.1197712
Median Absolute Deviation (MAD)0.5
Skewness0.39518554
Sum5929
Variance0.55968407
MonotonicityNot monotonic
2024-05-16T12:35:23.247061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2.5 732
26.2%
1 477
17.1%
1.75 384
13.7%
2 270
 
9.7%
2.25 259
 
9.3%
1.5 188
 
6.7%
2.75 149
 
5.3%
3 105
 
3.8%
3.25 76
 
2.7%
3.5 75
 
2.7%
Other values (11) 82
 
2.9%
ValueCountFrequency (%)
0.75 9
 
0.3%
1 477
17.1%
1.25 1
 
< 0.1%
1.5 188
 
6.7%
1.75 384
13.7%
2 270
 
9.7%
2.25 259
 
9.3%
2.5 732
26.2%
2.75 149
 
5.3%
3 105
 
3.8%
ValueCountFrequency (%)
5.75 1
 
< 0.1%
5.5 2
 
0.1%
5.25 1
 
< 0.1%
5 3
 
0.1%
4.75 3
 
0.1%
4.5 12
 
0.4%
4.25 12
 
0.4%
4 17
 
0.6%
3.75 21
 
0.8%
3.5 75
2.7%

sqftLiving
Real number (ℝ)

Distinct454
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2070.3843
Minimum370
Maximum6900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:24.731207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile950
Q11450
median1940
Q32540
95-th percentile3672
Maximum6900
Range6530
Interquartile range (IQR)1090

Descriptive statistics

Standard deviation860.19216
Coefficient of variation (CV)0.41547463
Kurtosis2.0459365
Mean2070.3843
Median Absolute Deviation (MAD)540
Skewness1.0951491
Sum5790865
Variance739930.55
MonotonicityNot monotonic
2024-05-16T12:35:25.013443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1680 22
 
0.8%
1660 21
 
0.8%
1940 21
 
0.8%
1300 21
 
0.8%
1200 20
 
0.7%
1800 20
 
0.7%
1500 19
 
0.7%
1480 19
 
0.7%
1840 19
 
0.7%
1230 19
 
0.7%
Other values (444) 2596
92.8%
ValueCountFrequency (%)
370 1
< 0.1%
420 1
< 0.1%
490 1
< 0.1%
520 1
< 0.1%
550 1
< 0.1%
560 1
< 0.1%
580 1
< 0.1%
590 2
0.1%
620 1
< 0.1%
630 1
< 0.1%
ValueCountFrequency (%)
6900 1
< 0.1%
6630 1
< 0.1%
6490 1
< 0.1%
6070 1
< 0.1%
6050 1
< 0.1%
6040 1
< 0.1%
5960 1
< 0.1%
5850 1
< 0.1%
5584 1
< 0.1%
5520 1
< 0.1%

sqftLot
Real number (ℝ)

Distinct2033
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15216.336
Minimum747
Maximum1074218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:25.349667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum747
5-th percentile1676.8
Q15000
median7688
Q311000
95-th percentile46405.8
Maximum1074218
Range1073471
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation37804.856
Coefficient of variation (CV)2.4844913
Kurtosis252.30781
Mean15216.336
Median Absolute Deviation (MAD)2743
Skewness12.157446
Sum42560093
Variance1.4292071 × 109
MonotonicityNot monotonic
2024-05-16T12:35:25.683088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 51
 
1.8%
6000 47
 
1.7%
7200 34
 
1.2%
4000 27
 
1.0%
8100 17
 
0.6%
7500 17
 
0.6%
4500 16
 
0.6%
4800 16
 
0.6%
3000 15
 
0.5%
8400 13
 
0.5%
Other values (2023) 2544
91.0%
ValueCountFrequency (%)
747 1
< 0.1%
750 1
< 0.1%
779 1
< 0.1%
833 1
< 0.1%
835 1
< 0.1%
844 1
< 0.1%
867 1
< 0.1%
868 1
< 0.1%
885 1
< 0.1%
889 1
< 0.1%
ValueCountFrequency (%)
1074218 1
< 0.1%
478288 1
< 0.1%
435600 1
< 0.1%
423838 1
< 0.1%
389126 1
< 0.1%
327135 1
< 0.1%
251341 1
< 0.1%
250470 1
< 0.1%
249126 1
< 0.1%
247421 1
< 0.1%

Floors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4944583
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:26.063909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53679495
Coefficient of variation (CV)0.35919031
Kurtosis-0.45752724
Mean1.4944583
Median Absolute Deviation (MAD)0.5
Skewness0.61901879
Sum4180
Variance0.28814882
MonotonicityNot monotonic
2024-05-16T12:35:26.497176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1365
48.8%
2 1052
37.6%
1.5 279
 
10.0%
3 78
 
2.8%
2.5 22
 
0.8%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
1 1365
48.8%
1.5 279
 
10.0%
2 1052
37.6%
2.5 22
 
0.8%
3 78
 
2.8%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
3.5 1
 
< 0.1%
3 78
 
2.8%
2.5 22
 
0.8%
2 1052
37.6%
1.5 279
 
10.0%
1 1365
48.8%

Waterfront
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size158.4 KiB
0
2781 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2797
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2781
99.4%
1 16
 
0.6%

Length

2024-05-16T12:35:26.903161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T12:35:27.299188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2781
99.4%
1 16
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 2781
99.4%
1 16
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2797
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2781
99.4%
1 16
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2797
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2781
99.4%
1 16
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2781
99.4%
1 16
 
0.6%

View
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size158.4 KiB
0
2534 
2
 
130
3
 
68
4
 
36
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2797
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2534
90.6%
2 130
 
4.6%
3 68
 
2.4%
4 36
 
1.3%
1 29
 
1.0%

Length

2024-05-16T12:35:27.482713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T12:35:27.791540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2534
90.6%
2 130
 
4.6%
3 68
 
2.4%
4 36
 
1.3%
1 29
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 2534
90.6%
2 130
 
4.6%
3 68
 
2.4%
4 36
 
1.3%
1 29
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2797
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2534
90.6%
2 130
 
4.6%
3 68
 
2.4%
4 36
 
1.3%
1 29
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2797
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2534
90.6%
2 130
 
4.6%
3 68
 
2.4%
4 36
 
1.3%
1 29
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2534
90.6%
2 130
 
4.6%
3 68
 
2.4%
4 36
 
1.3%
1 29
 
1.0%

Condition
Categorical

MISSING 

Distinct5
Distinct (%)0.2%
Missing31
Missing (%)1.1%
Memory size164.0 KiB
3.0
1743 
4.0
749 
5.0
253 
2.0
 
17
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8298
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 1743
62.3%
4.0 749
26.8%
5.0 253
 
9.0%
2.0 17
 
0.6%
1.0 4
 
0.1%
(Missing) 31
 
1.1%

Length

2024-05-16T12:35:28.005989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T12:35:28.189883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1743
63.0%
4.0 749
27.1%
5.0 253
 
9.1%
2.0 17
 
0.6%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 2766
33.3%
0 2766
33.3%
3 1743
21.0%
4 749
 
9.0%
5 253
 
3.0%
2 17
 
0.2%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5532
66.7%
Other Punctuation 2766
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2766
50.0%
3 1743
31.5%
4 749
 
13.5%
5 253
 
4.6%
2 17
 
0.3%
1 4
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8298
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2766
33.3%
0 2766
33.3%
3 1743
21.0%
4 749
 
9.0%
5 253
 
3.0%
2 17
 
0.2%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2766
33.3%
0 2766
33.3%
3 1743
21.0%
4 749
 
9.0%
5 253
 
3.0%
2 17
 
0.2%
1 4
 
< 0.1%

sqftAbove
Real number (ℝ)

Distinct412
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1762.0915
Minimum370
Maximum6070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:28.381871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile860
Q11170
median1540
Q32200
95-th percentile3302
Maximum6070
Range5700
Interquartile range (IQR)1030

Descriptive statistics

Standard deviation790.32153
Coefficient of variation (CV)0.44851333
Kurtosis1.6727722
Mean1762.0915
Median Absolute Deviation (MAD)450
Skewness1.21721
Sum4928570
Variance624608.13
MonotonicityNot monotonic
2024-05-16T12:35:28.582427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1140 33
 
1.2%
1200 31
 
1.1%
1300 31
 
1.1%
1320 30
 
1.1%
1010 30
 
1.1%
1050 28
 
1.0%
1400 27
 
1.0%
1680 24
 
0.9%
1240 24
 
0.9%
1090 24
 
0.9%
Other values (402) 2515
89.9%
ValueCountFrequency (%)
370 1
 
< 0.1%
420 1
 
< 0.1%
490 1
 
< 0.1%
520 1
 
< 0.1%
550 3
0.1%
560 1
 
< 0.1%
580 1
 
< 0.1%
590 2
0.1%
620 1
 
< 0.1%
630 3
0.1%
ValueCountFrequency (%)
6070 1
< 0.1%
6050 1
< 0.1%
5584 1
< 0.1%
5190 1
< 0.1%
5070 1
< 0.1%
4930 2
0.1%
4850 1
< 0.1%
4820 1
< 0.1%
4770 1
< 0.1%
4740 1
< 0.1%

sqftBase
Real number (ℝ)

ZEROS 

Distinct175
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.29281
Minimum0
Maximum2550
Zeros1650
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:28.790991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3610
95-th percentile1180
Maximum2550
Range2550
Interquartile range (IQR)610

Descriptive statistics

Standard deviation442.60477
Coefficient of variation (CV)1.4356636
Kurtosis0.88265297
Mean308.29281
Median Absolute Deviation (MAD)0
Skewness1.2895978
Sum862295
Variance195898.99
MonotonicityNot monotonic
2024-05-16T12:35:29.016076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1650
59.0%
500 33
 
1.2%
800 32
 
1.1%
600 28
 
1.0%
700 24
 
0.9%
400 23
 
0.8%
1000 21
 
0.8%
900 20
 
0.7%
300 17
 
0.6%
480 16
 
0.6%
Other values (165) 933
33.4%
ValueCountFrequency (%)
0 1650
59.0%
20 1
 
< 0.1%
50 1
 
< 0.1%
60 2
 
0.1%
65 1
 
< 0.1%
80 1
 
< 0.1%
90 2
 
0.1%
100 7
 
0.3%
110 1
 
< 0.1%
120 8
 
0.3%
ValueCountFrequency (%)
2550 1
< 0.1%
2300 1
< 0.1%
2150 2
0.1%
2080 1
< 0.1%
2070 1
< 0.1%
1950 2
0.1%
1940 1
< 0.1%
1910 1
< 0.1%
1860 1
< 0.1%
1840 1
< 0.1%

Yearbuilt
Real number (ℝ)

Distinct115
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1969.7762
Minimum1900
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:29.191546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1913
Q11950
median1973
Q31995
95-th percentile2008
Maximum2014
Range114
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.274488
Coefficient of variation (CV)0.014861835
Kurtosis-0.67477661
Mean1969.7762
Median Absolute Deviation (MAD)23
Skewness-0.47264833
Sum5509464
Variance856.99567
MonotonicityNot monotonic
2024-05-16T12:35:29.401992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 65
 
2.3%
2005 62
 
2.2%
2003 57
 
2.0%
2007 52
 
1.9%
2004 51
 
1.8%
2008 51
 
1.8%
1978 51
 
1.8%
1987 49
 
1.8%
1967 47
 
1.7%
1968 47
 
1.7%
Other values (105) 2265
81.0%
ValueCountFrequency (%)
1900 19
0.7%
1901 2
 
0.1%
1902 5
 
0.2%
1903 6
 
0.2%
1904 7
 
0.3%
1905 8
0.3%
1906 14
0.5%
1907 7
 
0.3%
1908 13
0.5%
1909 13
0.5%
ValueCountFrequency (%)
2014 21
 
0.8%
2013 26
 
0.9%
2012 17
 
0.6%
2011 17
 
0.6%
2010 14
 
0.5%
2009 31
1.1%
2008 51
1.8%
2007 52
1.9%
2006 65
2.3%
2005 62
2.2%

YrRenov
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean842.63675
Minimum0
Maximum2014
Zeros1616
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size43.7 KiB
2024-05-16T12:35:29.705789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32000
95-th percentile2011
Maximum2014
Range2014
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation985.94059
Coefficient of variation (CV)1.170066
Kurtosis-1.9011198
Mean842.63675
Median Absolute Deviation (MAD)0
Skewness0.31555658
Sum2356855
Variance972078.84
MonotonicityNot monotonic
2024-05-16T12:35:30.057019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1616
57.8%
2000 108
 
3.9%
2003 86
 
3.1%
2001 78
 
2.8%
2005 72
 
2.6%
2009 65
 
2.3%
2013 50
 
1.8%
2014 49
 
1.8%
2006 47
 
1.7%
2004 47
 
1.7%
Other values (44) 579
 
20.7%
ValueCountFrequency (%)
0 1616
57.8%
1912 17
 
0.6%
1913 1
 
< 0.1%
1923 26
 
0.9%
1934 4
 
0.1%
1945 2
 
0.1%
1948 1
 
< 0.1%
1953 1
 
< 0.1%
1954 3
 
0.1%
1955 2
 
0.1%
ValueCountFrequency (%)
2014 49
1.8%
2013 50
1.8%
2012 29
1.0%
2011 36
1.3%
2010 14
 
0.5%
2009 65
2.3%
2008 31
1.1%
2007 5
 
0.2%
2006 47
1.7%
2005 72
2.6%

City
Categorical

Distinct43
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size176.9 KiB
Seattle
993 
Renton
168 
Bellevue
159 
Redmond
142 
Kirkland
 
119
Other values (38)
1216 

Length

Max length19
Median length18
Mean length7.7504469
Min length4

Characters and Unicode

Total characters21678
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st rowSeattle
2nd rowSeattle
3rd rowRedmond
4th rowSammamish
5th rowRedmond

Common Values

ValueCountFrequency (%)
Seattle 993
35.5%
Renton 168
 
6.0%
Bellevue 159
 
5.7%
Redmond 142
 
5.1%
Kirkland 119
 
4.3%
Kent 110
 
3.9%
Sammamish 108
 
3.9%
Issaquah 107
 
3.8%
Auburn 100
 
3.6%
Shoreline 85
 
3.0%
Other values (33) 706
25.2%

Length

2024-05-16T12:35:30.398275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seattle 993
31.7%
renton 168
 
5.4%
bellevue 159
 
5.1%
redmond 142
 
4.5%
kirkland 119
 
3.8%
kent 110
 
3.5%
sammamish 108
 
3.4%
issaquah 107
 
3.4%
auburn 100
 
3.2%
shoreline 85
 
2.7%
Other values (46) 1040
33.2%

Most occurring characters

ValueCountFrequency (%)
e 3900
18.0%
t 2415
11.1%
l 2210
10.2%
a 2183
 
10.1%
n 1354
 
6.2%
S 1248
 
5.8%
o 853
 
3.9%
r 685
 
3.2%
i 675
 
3.1%
d 661
 
3.0%
Other values (35) 5494
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18193
83.9%
Uppercase Letter 3150
 
14.5%
Space Separator 334
 
1.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3900
21.4%
t 2415
13.3%
l 2210
12.1%
a 2183
12.0%
n 1354
 
7.4%
o 853
 
4.7%
r 685
 
3.8%
i 675
 
3.7%
d 661
 
3.6%
u 618
 
3.4%
Other values (14) 2639
14.5%
Uppercase Letter
ValueCountFrequency (%)
S 1248
39.6%
R 315
 
10.0%
B 273
 
8.7%
K 270
 
8.6%
I 151
 
4.8%
W 150
 
4.8%
M 136
 
4.3%
F 113
 
3.6%
A 104
 
3.3%
V 78
 
2.5%
Other values (9) 312
 
9.9%
Space Separator
ValueCountFrequency (%)
334
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21343
98.5%
Common 335
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3900
18.3%
t 2415
11.3%
l 2210
10.4%
a 2183
10.2%
n 1354
 
6.3%
S 1248
 
5.8%
o 853
 
4.0%
r 685
 
3.2%
i 675
 
3.2%
d 661
 
3.1%
Other values (33) 5159
24.2%
Common
ValueCountFrequency (%)
334
99.7%
- 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3900
18.0%
t 2415
11.1%
l 2210
10.2%
a 2183
 
10.1%
n 1354
 
6.2%
S 1248
 
5.8%
o 853
 
3.9%
r 685
 
3.2%
i 675
 
3.1%
d 661
 
3.0%
Other values (35) 5494
25.3%
Distinct76
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size177.5 KiB
2024-05-16T12:35:30.958260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters22376
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowWA 98144
2nd rowWA 98126
3rd rowWA 98053
4th rowWA 98074
5th rowWA 98052
ValueCountFrequency (%)
wa 2797
50.0%
98103 89
 
1.6%
98052 85
 
1.5%
98115 83
 
1.5%
98117 82
 
1.5%
98133 68
 
1.2%
98034 66
 
1.2%
98125 64
 
1.1%
98074 62
 
1.1%
98155 61
 
1.1%
Other values (67) 2137
38.2%
2024-05-16T12:35:32.026085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 3228
14.4%
9 3127
14.0%
W 2797
12.5%
A 2797
12.5%
2797
12.5%
0 2163
9.7%
1 1741
7.8%
5 783
 
3.5%
2 737
 
3.3%
3 703
 
3.1%
Other values (3) 1503
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13985
62.5%
Uppercase Letter 5594
 
25.0%
Space Separator 2797
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3228
23.1%
9 3127
22.4%
0 2163
15.5%
1 1741
12.4%
5 783
 
5.6%
2 737
 
5.3%
3 703
 
5.0%
7 532
 
3.8%
6 496
 
3.5%
4 475
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
W 2797
50.0%
A 2797
50.0%
Space Separator
ValueCountFrequency (%)
2797
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16782
75.0%
Latin 5594
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3228
19.2%
9 3127
18.6%
2797
16.7%
0 2163
12.9%
1 1741
10.4%
5 783
 
4.7%
2 737
 
4.4%
3 703
 
4.2%
7 532
 
3.2%
6 496
 
3.0%
Latin
ValueCountFrequency (%)
W 2797
50.0%
A 2797
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3228
14.4%
9 3127
14.0%
W 2797
12.5%
A 2797
12.5%
2797
12.5%
0 2163
9.7%
1 1741
7.8%
5 783
 
3.5%
2 737
 
3.3%
3 703
 
3.1%
Other values (3) 1503
6.7%

Interactions

2024-05-16T12:35:19.750998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:04.895949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:06.718640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:08.762601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:10.794677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:12.647218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:14.336438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:16.044652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:17.913754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:19.925249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:05.170841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:06.911226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:08.970662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:10.991210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:12.838448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:14.519669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:16.228529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:18.113826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:20.109095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:05.320992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:07.226726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:09.176787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:11.203325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:13.014383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:14.705430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:16.412334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:18.338551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:20.295470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:05.521346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:07.495476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:09.433060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:11.413428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:13.194423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:14.886733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:16.595242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:18.530290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:20.479164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:05.666234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:07.798374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:09.672823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:11.660070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:13.372874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:15.061811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:16.773851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:18.725952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:20.693402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:05.849628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:07.996611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:09.933094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:11.853824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:13.567786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:15.263762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:17.033065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:18.921684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:20.946608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:06.023151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:08.188492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:10.193108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:12.062435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:13.743512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:15.454644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:17.250598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:19.149982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:21.212887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:06.252856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:08.371315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:10.384946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:12.262804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:13.928709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:15.638640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:17.472272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:19.351111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:21.511910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:06.511281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:08.553417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:10.576670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:12.438267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:14.110566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:15.822277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:17.718208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T12:35:19.541447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-16T12:35:21.762596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-16T12:35:22.060165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

BedroomsBathroomssqftLivingsqftLotFloorsWaterfrontViewConditionsqftAbovesqftBaseYearbuiltYrRenovCityStateZip
439853.755340106552.5034.03740160019121989SeattleWA 98144
349543.50337050002.0023.0247090020080SeattleWA 98126
366821.001200247922.0002.01200019760RedmondWA 98053
330832.50179081442.0003.01790019890SammamishWA 98074
196942.752020107201.0004.0142060019761992RedmondWA 98052
188942.00212087011.5004.02120019602001Des MoinesWA 98198
111142.502630487062.0003.02630019860WoodinvilleWA 98072
331521.75167040081.0003.01670020050IssaquahWA 98029
128241.75181450001.0004.094487019511999SeattleWA 98115
265321.0074061801.0003.0740019481994SeattleWA 98118
BedroomsBathroomssqftLivingsqftLotFloorsWaterfrontViewConditionsqftAbovesqftBaseYearbuiltYrRenovCityStateZip
434042.50270088102.0003.02700020042003RedmondWA 98052
36142.75228028501.5004.0154074019300SeattleWA 98115
104322.50159026562.0003.0122037020090SeattleWA 98106
313142.50184055502.0003.01840020042003KentWA 98031
227542.00168050001.0003.098070019502005SeattleWA 98115
342141.50177057502.0003.01770019472012SeattleWA 98116
80932.00190066601.0005.095095019660Maple ValleyWA 98038
380342.50230338262.0003.02303020060AuburnWA 98092
315832.50254047752.0003.02540020060KentWA 98042
45132.00169095831.0004.01690019690RentonWA 98059

Duplicate rows

Most frequently occurring

BedroomsBathroomssqftLivingsqftLotFloorsWaterfrontViewConditionsqftAbovesqftBaseYearbuiltYrRenovCityStateZip# duplicates
032.5156042002.0003.01560020030Maple ValleyWA 980382
132.5180027002.0003.01800020110SeattleWA 981262